All posts by Amick Brown

Human Resources, Uncategorized

Where DOES the Time Go? Manage it and Feel Good, once and for all

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By Jenna Rosdahl, HR Manager, Amick Brown

Do you ever feel like there are not enough hours in the day to get everything done? Well you’re not the only one. Many of us find ourselves wishing for a few extra hours at the end of the day to finish our work, run errands, spend quality time with friends or family, or just relax. Twenty-four hours never seems like enough time to get it all done. Don’t agonize over what has not been finished – make sure you have time for the fun things in your life too with these good tips!

  1. Get a Good Night’s Sleep

Let’s be real – there is now an entire industry and field of medicine dedicated to sleep studies and overcoming sleep deprivation. Getting a full seven to nine hours of sleep per night will improve your mood and help you conquer your day. When you feel well rested you have more energy to tackle everything you need to get done and stay focused throughout the day, THEN have motivation to add activities that feel like a bonus. Consider sleep a noncaffienated remedy for better performance of memory and energy.  Throughout your day, you will be able to spend less time thinking of what you should be doing and more time completing tasks efficiently and effectively.

  1. The Early Bird gets the Worm

You know that saying, “the early bird gets the worm”. If you wake up two hours earlier than normal, you have two extra hours in your day to put towards doing what you think is most important to you. This extra time is YOURS. Do what makes you happy! If you are a night owl, you may find out it is better for you to stay up two hours later than normal, and accomplish a few tasks in the evening. If that is the case, take advantage of those few hours. All people are different. Some prefer waking up early and some prefer staying up later. Regardless, either will give you a few extra hours in the day to focus on what you want to get done – make it count for you on a personal level.

  1. Learn to say “No”

You are not Superman even though we sometimes wish we were.  It is important to know that you can’t do it all and that is OK.  You can try to do your best at accomplishing everything you are set out to do in a day, but cut yourself a little slack and say “no” when needed.  If not, this will lead to you getting frustrated or being stressed. “No”  is categorized as such a negative word, but it is not. It is better to give someone a realistic committment rather than promising something that you can’t get done. Manage your time by taking on tasks that you can complete and saying “no” to the tasks you can’t. Your team will appreciate the honesty.

  1. Love a List

I love lists! I admit it, call me crazy, but there is a real satisfaction in not only seeing what lies ahead but crossing those suckers off.  I suggest that you start your day by creating a task list. Your tasks can be as small as “make the bed” or as big as “develop an app”. Write it all down and prioritize your tasks, that way you can see everything that you want to achieve in the day/week.  This will give you a big picture of what your day will consist of and a roadmap on how to attack your day. Taking thirty minutes to do this at the beginning of your day is extremely helpful in managing your time and frankly, getting more done for work and play.

  1. Take a break when you hit a mental block or “just because”

Recently our office added a mid-day walk break for all that want to go. What a difference it made in productivity. Others in the office walk to lunch instead of driving and sitting. Get moving! The oxygen and blood flow hitting your brain resets the creativity, energy, and motivation for us.  It is not only almost impossible, but it is not good for your health to disregard breaks throughout the day. So, take a ten to fifteen minute break every few hours to help clear your mind, re-focus and complete your task more efficiently. Alternatively, take a mid-day walk or go to the gym at lunch. We all know that our minds can drift to different thoughts and ideas and this can prevent us from staying focused. By taking a break, we are allowing our minds to be free for a short amount of time which helps when going back to completing our tasks.

  1. Limit the amount of time spent on each task.

Do you ever start your day early in the morning and then look down at your clock and it’s almost 5:00pm and realize that you have been working on one single thing all day long? It happens to all of us, and yes, sometimes it is necessary to work on one thing all day long because it is important and needs to get done. But, be sure to not make a habit of spending all of your time on one thing. When you are sitting down to get something done, tell yourself that you are going to work on this for two hours or three hours, whatever works best for you. This will push you to stay focused for those few hours and can also help you be more efficient. Sometimes you won’t get the task completed in the timeframe you thought and that is ok. You can always go back to it, but setting a time limit will help you be more productive, therefore create a better outcome .

  1. Value, without exception, your contribution and personal time

We are not perfect. We lose track of time and our day gets away from us. By trying a few of these tips, you are likely to manage your time a lot better and accomplish everything you need and want to each day!

Analytics, Big data, Predictive Analytics, SAP

Reimagine Predictive Analytics for the Digital Enterprise

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future_predictive_analytics_SAPPHIRENOW

As part of a broad announcement made at SAPPHIRE NOW 2016, SAP announced a range of new features and capabilities in its analytics solutions portfolio. Because predictive capabilities play an important role in the portfolio, I thought I’d take this opportunity to share the details of our innovations in both SAP BusinessObjects Cloud and SAP BusinessObjects Predictive Analytics.

Innovations in SAP BusinessObjects Cloud

Predictive analytics capabilities have been added to the SAP BusinessObjects Cloud offering. Business users can use an intuitive graphical user interface to investigate business scenarios by leveraging powerful built-in algorithmic models. For example, users can perform financial projections with time series forecasts, automatically identify key influencers of operational performance, and determine factors impacting employee performance with guided machine discovery.

Learn more about our predictive capabilities in SAP BusinessObjects Cloud.

Innovations in SAP BusinessObjects Predictive Analytics

Predictive analytics features that aim to help analysts easily deliver predictive insights across an enterprise’s business processes and applications are planned for availability in the near term.

Planned innovations include:

  • Automated predictive analysis of Big Data with native Spark modeling in Hadoop environments
  • Enhancements for SAP HANA including in-database social network analysis and embedding expert model chains
  • A new simplified user interface for the predictive factory and automated generation of segmented forecast models
  • Integration of third-party tools and external processes into predictive factory workflows
  • The ability to create and manage customized models that detect complex fraud patterns for the SAP Fraud Management analytic application

Learn more about what SAP Predictive Analytics has in store.

Upcoming Release of SAP Predictive Analytics

Watch the video about our upcoming release of SAP Predictive Analytics for more information.

Thank you to Pierre Leroux, Director, Predictive Analytics Product Marketing, SAP for writing this informative article.

AmickBrown.com

 

Analytics, Business Intelligence, Predictive Analytics

The Human Aspect of Predictive Analytics

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By Ashith Bolar , Director AmBr Labs, Amick Brown

The past decade and a half has seen a steady increase in Business Intelligence (BI). Every company boasts a solid portfolio of BI software and applications. The fundamental feature of BI is Data Analytics. Corporations that boasts large data do indeed derive a lot of value from their Data Analytics. A natural progression of Data Analytics is Predictive Analytics (PA).

Think of data analytics as a forensic exercise in measuring the past and the current state of the system. Predictive Analytics is the extension of this exercise: Instead of just analyzing the past and evaluating the current, predictive analytics applies that insight to determine, or rather shape the future.

Predictive Analytics is the natural progression of BI.

The current state of PA in the general business world is, for the most part, at its inception. Experts in the field talk about PA as the panacea – as the be-all and end-all solution to all business problems. This is very reminiscent of the early stages of BI towards the turn of the century. Hype as it may be, BI did end up taking the center stage over the ensuing years. BI was not the solution to the business problems anymore; it was indeed mandatory for the very survival of a company. Companies don’t implement BI to be on the leading-edge of the industry anymore. They implement BI just to keep up. Without BI, most companies would not be competitive enough to survive the market forces.

Very soon, PA will be in a similar state. PA will not be the leading-edge paradigm to get a headstart over other companies. Instead PA is what you do to just survive. All technologies go through this phase transition – from leading-edge to must-have-to-survive. And PA is no different.

predict the future

Having made this prediction, let’s take a look at where PA stands. Some industries (and some organizations) have been using predictive analytics for several decades. One such not-so-obvious example is the financial industry. The ubiquity of FICO scores in our daily lives does not make it obvious, but they are predictive analytics at work. Your fico score predicts, with a certain degree of accuracy, the likelihood of you defaulting on a loan. A simple number, that may or may not be accurate in individual cases, arguably has been the fuel to the behemoth economic machinery of this country, saving trillions of dollars for the banking industry as well as the common people such as you and me.

Another example would be that of the marketing departments of large retail stores. They have put formal PA to use for several years now, in a variety of applications such as product placement, etc. If it works for them, there is no reason it should not work for you.

This is easier said than done. Implementing Predictive Analytics is not a trivial task. It’s not like you buy a piece of software from the Internet, install it on a laptop, and boom – you’re predicting the future. Although I have to admit that that is a good starting point. Implementing the initial infrastructure for PA does require meticulous planning. It’s a time-consuming effort, but at this point in time, a worthy effort.

Let’s take a look at high-level task list for this project

  1. Build the PA infrastructure
  2. Choose/build predictive model(s)
  3. Provision Data
  4. Predict!
  5. Ensure there’s company-wide adoption of the new predictive model. Make PA a key part of the organization’s operational framework. Ensure that folks in the company trust the predictive model and not try to override it with their human intelligence.

Steps 1 thru 4 are the easy bits. It’s the 5th step that requires a significant effort.

Most of us have relied on our superior intellect when it comes to making serious decisions. And most of us believe that such decision-making process yields the best decisions. It is hard for us to imagine that a few numbers and a simple algorithm would yield better decisions than those from the depths of our intellect.

However, it is important to change your organization’s mindset about predictive analytics. If you are considering your business to be consistently run on mathematical predictive models, acceptance from the user community is crucial. Implementing PA is a substantial effort in Organizational Change Management.

Remember the financial services industry. They don’t let their loan officers make spot decisions on the loan-eligibility of their clients based on their appearance, style of speech or any such human sensory cues. Although, if you ask the loan officer, they might claim to be better judges of character – the financial industry does not rely on their superior human intellect to measure the risk of loan default. Generally, a single 3-digit number makes that decision for them.

The next time you go to the supermarket for a loaf of bread, and return with a shopping cart full of merchandise that you serendipitously found on the way back from the bread aisle including the merchandise along the cash-counter, you can thank (or curse) the predictive analytics employed by the store headquarters located probably a thousand miles away from you.

AmickBrown.com – Get What You Expect

Business Intelligence, Programming, SAP

4 Best Practices to make your Storyboards more Dynamic and Appealing

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By Iver van de Zand  – Business Intelligence & Analytics – SAP – Visualization – DataViz – Evangelist – Author of “Passionate On Analytics”

Your end users will love it when you’d deliver your story- and dashboards in a more appealing and dynamic way. In these Let Me Guide series I discuss 4 easy to use best practices that will help you doing so:

  1. Using backgrounds

  2. Using Navigation

  3. dynamic Vector Diagram pictures: SVG

  4. Dynamic Text

Using Background

Backgrounds can better the looks and experience of story- and dashboards. Use the opacity to ensure the attention is not too much distracted from the actuals graphs and charts. I tends to create my backgrounds myself using PowerPoint: create a slide with a layout you like allocating space for KPI metrics and visualizations. Save the slide as JPG which you can import as background into SAP Lumira.

Using Navigation 

If you have story- or dashboards with multiple pages, my experience is that custom navigation buttons help you users finding what they should read. I use custom navigation all the time on my storyboard’s landing pages for example. Here is how you do it:

  •  Find a shape or picture that you want to use as clickable button and save it as xx.jpg

  • Import xx.jpg as picture in Lumira and drop it on your storyboard where you want it

  • Drag and drop a rectangle shape exactly over you newly created button and set its lines and fill-color both to “none”

  • Click you “invisible” shape and add the URL or page number to it

  • Save and preview

Example landing page B

example of navigation buttons

Example landing page A

Example landing page A

Example of a core layout of a landing page for your storyboard. The color-coded tiles can be used as navigation buttons. The generic tiles act to show key metrics info. Save the core lay-out as JPG and use this JPG as core background in your storyboard. Now add an object over the color coded sections, make it invisible and add a page-link to the appropriate page in your story.

SVG files

Especially infographics gain on weight and meaningfulness if you use dynamic pictures as part of your charts and graphs. Bar- and line charts in SAP Lumira have the possibility to change its regular column and markers into a dynamic pictogram. You can use the embedded pictograms but also add your own. The pictograms need to be in the SVG dynamic vector format. Search for pictures on Google with the “ filetype:SVG” string to find SVG’s. Save and import them to Lumira and change the graphs properties. The results are impressive. It is easy to create your own SVG files: I use PowerPoint to create my own pictures and save them to JPG. Using conversion tools easily creates an SVG that you can use as dynamic chart/graph picture in your storyboards.

Dynamic Text

Dynamic Text is a powerful way to improve context sensitive messaging in your story- and dashboards. The dynamic text is based on a dataset attributes and thus changes when data is refreshed are filtered. Since SAP Lumira handles the dynamic text as any other attribute, you can also apply formulas against the text.

 www.AmickBrown.com
Human Resources

Corporate Social Responsibility and Small Business

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by Karen Gildea , Managing Partner , Amick Brown

Sustainability has been a recognized business strategy for the past decade or so.  Strategies to reduce the adverse impact that a corporation had the on the environment were the areas of focus.  Choices to invest in renewable resources, to institute recycling policies and reduce contributions to the pollution of our air and water were key.

Over the past 10 years the objective of sustainability has been shifting from a direct focus on environmental impact to a more far reaching objective that still includes environmental goals, but has also brought broader social concerns into focus as well.

In addition to environmental impact, companies are now paying attention to their employee’s and customer’s well-being and the well-being of the communities that they operate in.  Corporate Social Responsibility or CSR, has become a key business strategy for many global companies with 75% of them tracking and issuing CSR reports.  It is time for all responsible companies, big and small to get on board as well.

The Society for Human Resource Management (SHRM) defines Corporate Social Responsibility as “Recognition of the impact a corporation has on the lives of its stakeholders (including shareholders, employees, communities, customers, and suppliers) and the environment; can include corporate governance, corporate philanthropy, sustainability, and employee rights and workplace safety.”

There is a generally recognized principle of the 3 P’s when considering Corporate Social Responsibility:

  • Profit (Economic) – a company must be profitable to be sustainable into the future. A profitable company produces products and services that provide benefit, they pay employees and purchase goods and services which is good for the economy.
  • Planet (Environmental) – a company should have a sustainability strategy to minimize its negative impact on the environment and expanding the use of renewable resources.
  • People (Social) – carrying for employees through pay and benefits, helping them achieve a work/life balance, providing growth opportunities and treating them fairly will make them more productive in the organization. This will create a more sustainable workforce.  A company should also expand its focus to its surrounding community, suppliers and customers – the goal of which is to create a sustainable customer base.

Andrew Savitz represents this concept most concisely in his book “The Triple Bottom Line ”“A sustainable corporation is one that creates profit for its shareholders while protecting the environment and improving the lives of those with whom it interacts.”

Companies do not need to be large corporations to pursue CSR objectives.  Small businesses can and should participate as well.  By including a focus on providing long term value in addition to the pursuit of growth and profits, businesses big and small can advance their CSR impact.

The best way to start is to begin thinking about it – document it as a business strategy.  Think about it in terms of the 3 P’s and start small and simple.  Some ideas:

  • Profit – this one need not be further addressed as it is already a primary objective
  • Planet – with a little thought and planning, small businesses can establish internal processes that support a sustainable environment – simple approaches include:
    • instituting recycling
    • consciously reducing energy usage
    • evaluating suppliers and goods purchased based on their sustainability efforts
  • People – evaluate internal HR policies and employee relations for improvement opportunities:
    • recognize the needs of the employees in balance with the needs of the business; (e.g., flexible work schedules, support training opportunities, etc.)
    • ensure labor compliant and non-discriminatory business practices
    • look for volunteer or charitable opportunities in the community

A move toward CSR does not need to have a negative financial impact on a small business.  There are things that can be done without added cost.  Give it some thought and start small.

Do well by doing good!

Analytics, Predictive Analytics

The Time to Change is Now

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 by Paul Pallath, PhD, Chief Data Scientist & Director,
Advanced  Analytics SAP

clock_calendar_moneyThe world is speeding ahead at a significant pace towards a major revolution—the data-driven economy.  Several data-driven start-ups in the last decade have become large corporations (Google, Facebook, Twitter), with billions of people reached and influenced by their innovations. Here is a list of the hottest start-ups that are looking to mature to the big league.

As the momentum is picking up, major organizations from different industry verticals are in a quest to exploit the opportunities that have arisen from the humongous amount of data that their business generates, directly and indirectly. Philip Evans, Senior Partner, Boston Consulting Group, discussed in his TED talk what businesses would look like in the future, and the impact that Big Data will have on business strategies.

Whether businesses want to use data to make the world a better place, to understand the wishes of customers before they’re expressed, to be more proactive than reactive in decision making based on predictive technologies, or something else, there are several challenges that we must all face.

These challenges include the following.

  1. Data volumes are ever-increasing. Most of the data is unstructured (either textual, videos, graphs and so on) rather than transactional and structured.
  2. The decision cycles are becoming shorter. We expect millisecond response times from the systems we interact with. And with mission-critical applications, the response time could be even shorter.
  3. Thousands of predictive models are required to get coverage of all the predictive scenarios that an application can create.
  4. Traditional methods of modelling are very time-consuming. The quest to find a perfect model drains valuable time and money before it can be put to business use.
  5. The knowledge workers who understand data science, and who could mine useful actionable nuggets from the data, are rare. The demand for such skilled workers is ever-increasing and their lack of availability is causing a massive skills gap.

With Challenges Comes Opportunities

However, with challenges come opportunities.

Consider the Industrial Revolution. As we know, at that point in history the move was to automate processes that were repetitive or required more manual effort, and find ways to free valuable resources—the brain and imagination—that we use to focus on even larger problems. The result is the modern world we now live in.

Now the data revolution is demanding a new change. That is, the way in which we work with data. We must find ways and means to automate most of the repetitive workflows and modelling processes that are applicable industry-wide. This way, we can free the very valuable time of the data scientist to focus on tough problems that cannot be solved without human intervention.

With several thousand models that enable a data-driven company to run, it’s also important to have capabilities that enable the company to monitor the performance of these models in real time. This means decommissioning the models that exhibit significant deviation in performance, as compared to when they were deployed on production systems.

This paves the way for the need of a Massive Predictive Factory, a single source of truth and heart-beat monitor for the entire organization.

For more on Predictive Analytics, Follow Amick Brown

Analytics, Big data, SAP

Predictive Analytics and the Segment of One

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by Richard Mooney,                                           Product Manager, Advanced Analytics, SAP

 

Woman Buying Clothes --- Image by © Tim Pannell/CorbisOne of the areas that SAP is investing heavily in is the idea of providing ‘extreme customer experience’ to the ‘Segment of One.’  What does this mean for analytics? Traditionally, large enterprises split customers into multiple segments based on customer attributes that were then used to identify and classify customers.  These segments included their location, their current and potential spend, and which products and options they chose when they became a customer.

Marketers use these segments to determine which products they would market to which customers. Likewise, customer support applies different levels of service to each customer segment, and operations measures the profitability of each segment separately.

This is both highly frustrating to customers and an incredibly inefficient use of resources.

  • Every customer is different. They feel frustrated when their individual needs aren’t met, and their expectations about how they’re treated as customers are rising.
  • A one-size-fits-all approach doesn’t take into account the emerging customer acquisition and support channels that provide the potential to reduce the cost of service and market much more effectively. This includes mobiles applications, social networks, and the internet of things.
  • Because the cost of customer communication is plummeting, customers are inundated with content. They’re choosing to delete, unfollow and unsubscribe from content that doesn’t speak to them.

These same trends are opportunities. Companies are collecting far more information than ever before and the technology exists to leverage this at scale.  They no longer need to treat customers as being pure segments.  They can market to them personally, understand their likes and preferences, and give them services, all of which turns them into fans and advocates.

So How Do We Use Data to Connect to the Segment of One?

  • Make the Segment of One a corporate mandate. Communicate and service each customer as if it were a personal connection.
  • Rethink how your digital front office assets (including digital marketing, customer service and online) interact with customers to support this mandate.
  • Build a team of data scientists and data analysts to move from guesswork to data-driven decision making.
  • Build your customer communication around their analysis and deploy their work into every front office application. Measure and monitor the return on investment (ROI) from each initiative.

Done properly, this will result in happier customers and higher net promoter scores.   It also means that the data companies are collecting results in visible ROI, which improves their bottom line.

We would love to hear your thoughts on how the Segment of One will drive your data strategy.  Contact us or comment here to let us know.

 

Analytics, Big data, SAP

Customer Analytics – Predictive at the Center of Everything You Do

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Many Thanks to Chandran Saravana, Senior Director, Predictive Analytics Product Marketing, SAP

crystal-ballAs we discussed earlier, digital transformation is about taking a holistic approach to transforming the customer experience in all aspects. In this way, you fundamentally create new business models where the convergence of physical and digital occurs at the highest level.

In today’s quickly changing and evolving digital marketplace, there are a few macro trends that you see.  The purchase path is not linear, customer engagement occurs through many touch points, and customers expect each interaction to be relevant and personalized.

The rules of engagement are changing and businesses are no longer the first stop for customers when they want to inform themselves.  People now turn to their networks to get information and recommendations. To meet the expectations of your customers today, you need to go beyond the traditional approach of acquiring and retaining customers.

The goal today is to deliver an amazing customer experience at every interaction so that you not only acquire and retain customers, but you also gain  customers who become your loyal fans, your outspoken advocates. This all boils down to the fundamental question,

How can I personalize every customer interaction across multiple channels in real time by analyzing Big Data?

This leads to transforming the operating models to a more customer-centric, integrated organization using data and analytics as the basis for decision making. The transformation can be started with very simple questions:

  • Who are my “best” customers?
  • Who should I target for promotion?
  • How should I vary my promotion to different customers?
  • What product should I sell at what channel and at what price?
  • How should I keep the supply and demand in absolute balance?

A series of predictive models for each one of these questions will establish a framework to manage the customer life cycle. To achieve this, you  use a variety of techniques, such as segmentation, link analysis, propensity, forecasting and more.

What do you think? We look forward to hearing from you.

See more about Amick Brown , SAP Silver Services Partner

 

Analytics, Business Intelligence, Programming, SAP

Brainteaser: Storyboard or Dashboard…Self-Service or Managed…you choose

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By Iver Van de Zand, SAP

If there is one term that always is food for discussion when I talk to customers, it is definitely “dashboard”. What exactly is a dashboard, how close is it to a storyboard, are dashboard only on summarized data and when to use a dashboard versus a storyboard. Tons of questions that already start in a bad shape because people have other perceptions of what a dashboard really is. And let’s be honest; take a canvas, put a few pies on it and a bar-chart, and people will already mention it as a dashboard. Let’s see whether we can fine-tune this discussion a bit.

A Dashboard

A business intelligence dashboard is a data visualization technique that displays the current status and/or historical trends of metrics and key performance indicators (KPIs) for an enterprise. Dashboards consolidate and arrange numbers, metrics and sometimes performance scorecards on a single screen. They may be tailored for a specific role and display metrics targeted for a single point of view or department. The essential features of a BI dashboard product include a customizable interface and the ability to pull real-time data from multiple sources. The latter is important since lots of people think dashboards are only on summarized data which is absolutely not the case; dashboards consolidate data which may be of the lowest grain available! Key properties of a dashboard are:

  1. Simple and communicates easily and straight

  2. Minimum distractions, since these could cause confusion

  3. Supports organized business with meaning, insights and useful data or information

  4. Applies human visual perception to visual presentation of information: colors play a significant role here

  5. Limited interactivity: filtering, sorting, what-if scenarios, drill down capabilities and sometimes some self-service features

  6. They are often “managed” in a sense that the dashboards are centrally developed by ICT, key users or a competence center, and they are consumed by the end-users

  7. Offer connectivity capabilities to other BI components for providing more detail. Often these are reports with are connected via query-parsing to the dashboards

A Storyboard

Is there a big difference between a storyboard and a dashboard? Mwah, not too much: they both focus on communicating key – consolidated – information in a highly visualized and way which ultimately leaves little room for misinterpretation. For both the same key words apply: simple, visual, minimum distraction.

The main difference between a dashboard and a storyboard is that the latter is fully interactive for the end user. The interactivity of the storyboard is reflected through capabilities for the end user to:

  • Sort

  • Filter data: include and exclude data

  • Change chart or graph types on the fly

  • Add new visualizations on the fly; store and share them

  • Drill down

  • Add or adjust calculated measures and dimensions

  • Add new data via wrangling, blending or joining

  • Adjust the full layout of the board

  • Create custom hierarchies or custom groupings

  • Allow for basic data quality improvements (rename, concatenate, upper and lower case etc)

Another big difference between dashboards and storyboards is that storyboards are self-service enabled boards meaning the end user creates them him/herself. Opposite to dashboards that are typically “managed” and as such are created centrally by ICT, key users or a BICC, and are consumed by the end user.

A Dashboard versus a Storyboard

So your question, dear reader, is “what is the day-to-day difference and what to you use when”? Well the answer is in the naming of both boards:

The purpose of a storyboard is to TELL A STORY: the user selects a certain scope of data (which might be blended upon various sources) and builds up a story around that data that provides insights in it from various perspectives. All in a governed way of course. The story is built upon various visualizations that are grouped together on the canvas of the storyboard. These visualizations can be interdependent – filtering on one affects the others – or not. The canvas is further enriched with comments, text, links or dynamic pictures … all with the purpose to complete the story.

Storyboarding has dramatically changed day-to-day business: the statement “your meeting will never be the same” applies definitely. Your meetings are now being prepared by creating a storyboard; meetings are held using storyboards to discuss on topics and make funded decisions, simulations on alternative decisions are done during the meetings using the storyboards and final conclusions can be shared via the storyboards. Governed, funded, based on real-insights!

A dashboard has a pattern of analyzing that is defined upfront. It is about KPI’s or trends of a certain domain, and you as a user consume that information. You can filter, sort or even drill down in the data, but you cannot change the core topic of data. If the KPI’s are on purchasing information, it is on purchasing information and stays like it. You neither can add data to compare it.

In a number of situations one does not want the end user to “interact” with the information since it is corporate fixed data that is shared on a frequent and consistent time. Enterprises want that information to be shared for insights in a consistent, regular and recognizable way. Users will recognize the dashboard, consume the information and – hopefully – act upon it. Think for example about weekly or monthly performance dashboards, or HR dashboards that provide insights in attrition on recurring moments in time.

Dashboards and Storyboards: the “SAP way”

The nuances made above on dashboards and storyboards are being reflected in SAP’s Business Intelligence Suite. Its component Design Studio is a definite managed dashboarding tool. Extremely capable of visualizing insights in a simple and highly attractive way while in the meantime able to have online connections to in-memory data sources, SAP BW or semantic layers. Storyboarding is offered via the on-premise SAP Lumira or via Cloud through the Cloud for Analyticscomponent.

If you have difficulties deciding what to offer to your end users, the BI Componentselection tool I made easily helps you understanding whether your users require dashboards or/and storyboards. You might want to try it.

Financial storyboard

Financial storyboard

Self-service storyboard created in around 45 minutes using SAP Lumira. On this page the heat-map section that allows for white spot analyses. Data can be exported at any time. User has numerous capabilities to add data, visualizations and additional pages

Retailing Dashboard

Financial storyboard

Financial storyboard

Self-service storyboard created in around 45 minutes using SAP Lumira. User has numerous capabilities to add data, visualizations and additional pages

SAP

The Art and Science of Customer Empathy in Design Thinking

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by Kaan Turnali, Global Senior Director, Enterprise Analytics

SAPVoice+Art+And+Science+of+Empathy+Design+Thinking+by+Kaan+TurnaliCustomer-centric solutions demand empathy. But, how we employ this principle within design thinking is as critical—if not more—as what we do in the process.

Certain slices can be easily repeated—that’s the science part. However, not everything fits neatly into a template. More than anything else, we rely on our creativity to accurately frame a problem and discover the attached opportunity. That’s the art of customer empathy within design thinking.

In my previous post, I discussed three factors that are critical turning empathy into an obsession when developing customer-centric innovations. I want to expand on this topic and elaborate on how it can also be applied to our everyday work as well.

Customer Empathy is not Inherited or Repeated—it’s Continuously Learned

The unconditional act of projecting ourselves into our customers’ (or users’) shoes has to be unreserved. Empathy works only if we open up our nerve endings and feel what it is like to be in another’s shoes. One of the key approaches is adopting a beginner’s mindset that functions as a reset button—enabling us to experience a product or service as if it’s the first time we are using it.

In human-centered design, we use a set of tools to observe and communicate with people and better understand their journey. Empathetic listening and observation are essential during the entire design process:

  • Immersion: Place ourselves in the full experience through the eyes of the user.
  • Observation: Carefully watch and examine what people are actually doing.
  • Conversation: Accurately capture conversations and personal stories.

All three approaches require focus and precision because they typically produce different insights. To learn, we must listen more than we talk. When we observe, we disappear, rather than interfere. There is no room for sharing our opinions or selling the solution. We want facts. If we can’t understand the “why” behind an experience or problem, any assumptions about the “what” and “how” become skewed or misleading.

Our Knowledge is the Source of our Bias—Sometimes

In design, what we know can be just as detrimental as what we don’t know. One of the best examples of this reality is seen in technology projects.

Senior developers cooped up in a lab can produce very sophisticated code. These teams develop customer-facing elements based on a bias that reflects their extensive knowledge of the technology while ignoring steps considered minor from their vantage point. However, these minor steps are indispensable to users who are not necessarily tech-savvy—which may make up the majority of their customer base.

By simply leaving out parts that they consider obvious corners, these teams may not observe or attempt to live the experience through the eyes of the actual user—missing out on the opportunity to create a well-rounded, customer-centric experience.

With design thinking, we always insist on seeking untested experiences so we can capture unrefined observations that frame the details of the user journey.

“Emosurances” Influence our Perception of a Product or Service

Humans tend to react to emotional assurances (emosurances). They play a crucial role in designing a human-centered user experience—especially the user interface (UI).

For example, consider the experience with a digital process or transaction:

  • How many times do you find yourself in a state of uncertainty?
  • Do you know where you are in the process or queue?
  • Will you get an alert when it’s completed?
  • Will you abandon it because you are unsure of the next step?
  • Are you given any visual feedback, such as a progress bar?
  • If there is service interruption, do you get a notification? Is the message clear enough that it does not require further translation to understand next steps?

The scenarios are endless and apply to any user experience—digital or analog, online or in person. And even though these questions appear mechanical and a matter of UI, tackling these emosurances proactively is at the core of the empathy principle.

Bottom Line

The traditional value proposition of a product or service is a promise of particular utility value. If you get X, you will receive Y as a result of Z.

The design-thinking value proposition is a promise of core values: You want to get X because you care about Y and Z matters to you.

The actual value of the empathy principle comes from understanding our customers’ 360-degree viewpoint, especially their emotional attachments. Then, we can deliver a compelling value proposition that guides us along the innovation path. This approach enables a forward-thinking mindset that fuels a cultural shift paramount for competing on design thinking.

Stay tuned for the next installment of the Design Thinking thought leadership series!

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For more information on Design Thinking, contact our team at Amick Brown